Hit Predictor
Yesterday I was talking to my friend and colleague Pau Rausell-Köster, from the Research Unit in Cultural Economics (Universitat de València), about the Netflix Prize. We were discussing about the foundations of taste and preferences, and how it was quite difficult to, by means of a devil reductionism, create a mathematical model that could predict how you’re going to rate a movie. The question was: it works!.
This conversation though led to another mathematical model it’s been used for a while by a company called Polyphonic HMI S.L. to predict if a song will be successful (aka “a HIT”). They use a methodology they have named as “Hit Song Science”, which basically uses “Spectral Decomposition” to get different musical attributes for all the songs they have analysed (3.5 million to date). They, they apply clustering techniques to the songs that have been a success (aka “a HIT”) in the last 5 years (I imagine, the time-frame is just to take out the trends and account for changes/evolution in people’s preferences). Then, they are able to predict if a new song will succeed in the market and they asign a rating (controlling type-I error).
There’s only a downsize: would the record companies invest in promoting songs with low rating?. This would affect the song to the extent of not helping it to become a hit, so, again our beloved maths would be changing the course of events and distorting the model by means of the feedback in flawed data (the reverse, type-II error, could as well happen, bad songs evaluated as possible hits being highly promoted and succeeding). Moreover, if this happens to be in a big scale, innovation in music creation is aborted…, unless… you’re brave and forget the model!.
PS For the Netflix Prize Teams: food for thought.
